This grant provides funding for the development of a quantitative decision making framework for Supply Chain Revenue Management (SCRM): Application of revenue management (RM) in the world of physical goods under multiple resources that can be utilized flexibly for satisfying the market differentiated stochastic demands for products with short life cycles. The particular settings of interest include, but are not restricted to, the custom (contract) manufacturing and e-commerce industries. Complex features of the underlying supply chains in these industries"such as geographically dispersed multiple suppliers/facilities and customers, stochastic delivery/production lead times, and economies of scale inherent in transportation and production"impose a barrier against the immediate use of existing RM techniques. Hence, the goal is to develop quantitative models for dynamic resource allocation and pricing decisions in order to mitigate profit-at-risk by aligning demand and supply in realistic supply chain settings. The methodological research focuses on stochastic dynamic optimization models and effective solution algorithms.
If successful, this research will i) deliver analytical models for dynamic resource allocation and pricing problems characterized by multiple limited resource capacities over finite selling horizons and market differentiated stochastic demands for products; ii) identify optimal SCRM policies and develop computational procedures for their implementation that will serve as online decision support tools; iii) quantify the benefits of SCRM and, hence, the use of advanced information technology enabling effective SCRM practices; and iv) enrich the theory of RM as applied to realistic supply chain settings for efficient industry implementation and economic impact.
